# Creativity in translation: machine translation as a constraint for literary texts

arXiv: https://arxiv.org/abs/2204.05655 · HTML: https://mlsanity.com/paper/2204.05655/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Impact of Post-editing and Machine Translation on Creativity and Reading Experience](https://mlsanity.com/paper/2101.06125/) (2021)
- [Machine Translation of Novels in the Age of Transformer](https://mlsanity.com/paper/2011.14979/) (2020)

## Built on by (most cited first, 10 of 10)

- [How Good Are LLMs for Literary Translation, Really? Literary Translation Evaluation with Humans and LLMs](https://mlsanity.com/paper/2410.18697/) (2024)
- [Extending CREAMT: Leveraging Large Language Models for Literary Translation Post-Editing](https://mlsanity.com/paper/2504.03045/) (2025)
- [Multi-Figurative Language Generation](https://mlsanity.com/paper/2209.01835/) (2022)
- [To MT or not to MT: An eye-tracking study on the reception by Dutch readers of different translation and creativity levels](https://mlsanity.com/paper/2504.19850/) (2025)
- [Creativity Bias: How Machine Evaluation Struggles with Creativity in Literary Translations](https://mlsanity.com/paper/2605.13596/) (2026)
- [To be or not to be: a translation reception study of a literary text translated into Dutch and Catalan using machine translation](https://mlsanity.com/paper/2307.02358/) (2023)
- [Missing the human touch? A computational stylometry analysis of GPT-4 translations of online Chinese literature](https://mlsanity.com/paper/2506.13013/) (2025)
- [AI Technologies in Language Access: Attitudes Towards AI and the Human Value of Language Access Managers](https://mlsanity.com/paper/2605.19234/) (2026)
- [Metaphors in Literary Post-Editing: Opening Pandora's Box?](https://mlsanity.com/paper/2605.21178/) (2026)
- [Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach](https://mlsanity.com/paper/2606.05924/) (2026)

## Abstract (arXiv)

This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translation (MT), post-editing (PE) and translation without aid (HT). Our aim is to explore creativity, understood to involve novelty and acceptability, from a quantitative perspective. The results show that HT has the highest creativity score, followed by PE, and lastly, MT, and this is unanimous from all reviewers. A neural MT system trained on literary data does not currently have the necessary capabilities for a creative translation; it renders literal solutions to translation problems. More importantly, using MT to post-edit raw output constrains the creativity of translators, resulting in a poorer translation often not fit for publication, according to experts.